NETT Regularization for Compressed Sensing Photoacoustic Tomography

We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with Deep Neural Networks (2018), arXiv:1803.00092], whic...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Authors: Antholzer, Stephan, Schwab, Johannens, Bauer-Marschallinger, Johannes, Burgholzer, Peter, Haltmeier, Markus
Format: Article
Language:English
Subjects:
Computer simulation
Image reconstruction
Inverse problems
Machine learning
Neural networks
Photoacoustic effect
Regularization
Teaching methods
Tomography
Online Access:Get full text
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Summary:We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with Deep Neural Networks (2018), arXiv:1803.00092], which is based on a learned regularizer, for the first time to the CS-PAT problem. We propose a network architecture and training strategy for the NETT that we expect to be useful for other inverse problems as well. All algorithms are compared and evaluated on simulated data, and validated using experimental data for two different types of phantoms. The results on the one the hand indicate great potential of deep learning methods, and on the other hand show that significant future work is required to improve their performance on real-word data.
ISSN:2331-8422